@inproceedings{oai:jaxa.repo.nii.ac.jp:00038821,
 author = {西村, 尚樹 and 中島, 佑太 and 高田, 昇 and 矢入, 健久 and 武石, 直也 and 秋元, 康佑 and Nishimura, Naoki and Nakajima, Yuta and Takata, Noboru and Yairi, Takehisa and Takeishi, Naoya and Akimoto, Kosuke},
 book = {第59回宇宙科学技術連合講演会講演集, Proceedings of 59th Space Sciences and Technology Conference},
 month = {Oct},
 note = {第59回宇宙科学技術連合講演会（2015年10月7日-9日. かごしま県民交流センター), 鹿児島市, 鹿児島, We have studied learning-based telemetry monitoring system using machine learning, for the acquisition of high-performance fault diagnosis technology that leads to the stable operation of the satellite. In consideration of the past, we confirmed the effectiveness of the anomaly detection. Currently, in order to improve the detection rate of satellite state change that is difficult to be detected like precursory phenomenon of abnormality, we are considering the improvement of the analytical method. This paper describes the performance evaluation of telemetry monitoring and anomaly detection system based on data mining and machine learning which was carried out using the operating system of small satellite SDS-4., 形態: カラー図版あり, Physical characteristics: Original contains color illustrations, 資料番号: AC1600090000, レポート番号: JSASS-2015-4141},
 publisher = {日本航空宇宙学会(JSASS), The Japan Society for Aeronautical and Space Sciences (JSASS)},
 title = {SDS-4運用における学習型テレメトリ監視システムの性能向上検討(2): 検証と評価},
 year = {2015}
}